IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation
Yan, Lan1,2; Zheng, Wenbo1,3; Gou, Chao4; Wang, Fei-Yue1
Source PublicationKNOWLEDGE-BASED SYSTEMS
ISSN0950-7051
2022-04-06
Volume241Pages:11
Abstract

Photo-to-caricature translation is an extremely challenging task because there are not only texture differences between caricatures and photos, but also various spatial deformations in caricatures. Most of existing methods tend to introduce difficult obtained additional information such as precise facial landmarks to guide caricature generation. In addition, identity preservation is a crucial characteristic of caricatures, but unfortunately there seems to be few methods to consider it. Motivated by the aforementioned observations, we propose an Identity-Preservation Generative Adversarial Network (IPGAN) for unsupervised photo-to-caricature translation. In particular, considering the importance of identity retention, we propose a novel identity preservation loss to hold the identity information of original photos and improve the quality of generated caricatures. To capture realistic caricature styles, we design a style differentiation loss to help our model produce caricatures with styles that remarkably differ from photos. Moreover, to learn satisfactory deformations without supervision, our model uses a warp controller to acquire exaggerations automatically that enable to customize diverse exaggerations. As an unsupervised translation method, our IPGAN can also be applied to caricature to-photo translation. Experiments on the WebCaricature dataset suggest that our IPGAN achieves state-of-the-art performance and can generate realistic as well as identity preservation caricatures. 

KeywordPhoto-to-caricature translation Generative adversarial networks Image-to-image translation Style transfer Image warping
DOI10.1016/j.knosys.2022.108223
WOS KeywordIMAGE ; FACES
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2018AAA0101502] ; Key Research and Devel-opment Program of Guangzhou, China[202007050002] ; Natural Science Foundation of China[61806198] ; Natural Science Foundation of China[U1811463]
Funding OrganizationNational Key R&D Program of China ; Key Research and Devel-opment Program of Guangzhou, China ; Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000788730900008
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48441
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorGou, Chao
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Xi An Jiao Tong Univ, Sch Software Engn, Xian, Peoples R China
4.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yan, Lan,Zheng, Wenbo,Gou, Chao,et al. IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation[J]. KNOWLEDGE-BASED SYSTEMS,2022,241:11.
APA Yan, Lan,Zheng, Wenbo,Gou, Chao,&Wang, Fei-Yue.(2022).IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation.KNOWLEDGE-BASED SYSTEMS,241,11.
MLA Yan, Lan,et al."IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation".KNOWLEDGE-BASED SYSTEMS 241(2022):11.
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